Single-pixel spectral imaging technology based on sorting coding and depth learning

The computational spectral imaging technology can increase imaging rates without sacrificing image quality. Because the imaging speed and the imaging spectrum of single-pixel spectral imaging technology is fast and wide, it is a promising imaging technology in the future. Here, we proposed a single-pixel spectral imaging system based on sorting coding and depth learning. We used a Hadamard matrix based on Cake-cutting sorting to reduce the sampling rate and consequently to get higher image reconstruction contributions. Meanwhile the Fourier Frequency Domain Regularization and Spectral self-attention Transformer was used to improve the image quality. A 16-channel spectral image is simulated, and the visual quality and spectral curve of the reconstructed image are analyzed. The simulation results show that compared to the traditional system, the new system has improved the speed and quality of imaging. The imaging speed is about 30 times faster than conventional systems.